(Livestream Replay) Optimizing Your Report with the Report Analyzer Tool! - with Michael Kovalsky

ABSTRACT 📝

In this livestream, we will explore the Report Analyzer tool, an open-source external tool for analyzing and improving Power BI reports. This tool is designed to help Power BI developers/admins match Visual IDs from Log Analytics to specific visuals in their Power BI report. Additionally, Report Analyzer provides a visual interface for prioritizing performance troubleshooting. Learn how to fully take advantage of this tool in order to effectively design performant Power BI reports.

GUEST BIO- Michael Kovalsky 👤

As a member of the Power BI Customer Advisory Team (CAT) at Microsoft, Michael advises top enterprise customers with regard to data modeling, DAX optimization, performance optimization, best practices, and optimizing development processes. His experience in Power BI stems from years developing highly sophisticated tabular models for Microsoft's internal reporting - taking Power BI to the max. Michael has also created a plethora of tools and scripts for the community (which are available on his GitHub page) with the goal of empowering Power BI users with more advanced capabilities.

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Using Small Multiples with Independent & Non-Additive Categories

Video by: Reid Havens

Learn about a unique way to implement small multiples when you want to visualize data for independent and non-additive categories. With the benefit of this being able to use a single visual, and have much faster page load performance. It's not without some setup and design limitations though, so check out this video to learn more.

Calculating the Top Name & Value Using [DAX] in Power BI

Video by: Reid Havens

Learn how to leverage a long standing DAX pattern to calculate the top performing name/category and it's corresponding value using DAX measures in Power BI. Plus also see how to optimize the code using DAX Studio.

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(Livestream Replay) Semantics of DAX Queries & Caveats to Composite Models - with Jeffrey Wang

The popularity of Power BI has increased dramatically in the past few years. I am seeing an increasing number of enterprise customers who built complex composite models combining import tables with DirectQuery tables. Many users have asked questions on how DAX queries and measures are translated into remote SQL/MDX/DAX queries. To the surprise of a lot of people who have a SQL background, the semantics of DAX queries is very different from that of SQL queries even though both are used to produce the right data for the same visualizations. I am going to explain why the semantics of DAX queries poses unique challenges to DAX engine and how the latter employs myriads of optimizations to deliver good query performance in common scenarios. I am also going to explain how the semantics of DAX queries complicates query generations for composite models, demonstrate some of the issues most frequently encountered by the composite model users, and describe design principles to avoid the pitfalls.

GUEST BIO 📄

Jeffrey joined Microsoft SQL Server Analysis Services team in 2004 and contributed to the revolutionary transformation of Microsoft BI from multi-dimensional model and MDX language to tabular model and DAX language. He was one of the inventors of the DAX programming language in 2009 and have been driving the evolution of the DAX language ever since. Currently he is an engineering manager focusing on the development of DAX engine, query optimizer, DirectQuery, composite models, etc. Right now his team is putting the finishing touches on the GA of DirectQuery to PowerBI datasets.

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